BlueMonday1984Stubsack: weekly thread for sneers not worth an entire post, week ending 1st March 2026
ArchiteuthisI like how even by ACX standards scoot’s posts on AI are pure brain damage
One level lower down, your brain was shaped by next-sense-datum prediction - partly you learned how to do addition because only the mechanism of addition correctly predicted the next word out of your teacher’s mouth when she said “three plus three is . . . “ (it’s more complicated than this, sorry, but this oversimplification is basically true). But you don’t feel like you’re predicting anything when you’re doing a math problem. You’re just doing good, normal mathematical steps, like reciting “P.E.M.D.A.S.” to yourself and carrying the one.
The most compelling analogy: this is like expecting humans to be “just survival-and-reproduction machines” because survival and reproduction were the optimization criteria in our evolutionary history. […] This simple analogy is slightly off, because it’s confusing two optimization levels: the outer optimization level (in humans, evolution optimizing for reproduction; in AIs, companies optimizing for profit) with the inner optimization level (in humans, next-sense-datum prediction; in AIs, next-token prediction). But the stochastic parrot people probably haven’t gotten to the point where they learn that humans are next sense-datum predictors, so the evolution/reproduction one above might make a better didactic tool.
He also threatens an Anti-Stochatic-Parrot FAQ. Here’s hoping if this happens Bender et al enthusiastically point out this is coming from a guy whose long term master plan is to fight evil AI with eugenics.
Nonsensical analogies are always improved by adding a chart with colorful boxes and arrows going between them. Of course, the burden of proof is on you, dear reader, to explain why the analogy doesn’t make sense, not on the author to provide more justification than waving his hands really really hard.
Many of these analogies are bad as, I don’t know, “Denmark and North Korea are the same because they both have governments” or something. Humans and LLMs both produce sequences of words, where the next word depends in some way on the previous words, so they are basically the same (and you can call this “predicting” the next word as a rhetorical flourish). Yeah, what a revolutionary concept, knowing that both humans and LLMs follow the laws of time and causality. And as we know, evolution “optimizes” for reproduction, and that’s why there are only bacteria around (they can reproduce every 20 minutes). He has to be careful, these types of dumbass “optimization” interpretations of evolution that arose in the late 1800s led to horrible ideas about race science … wait a minute …
He isn’t even trying with the yellow and orange boxes. What the fuck do “high-D toroidal attractor manifolds” and “6D helical manifolds” have to do with anything? Why are they there? And he really thinks he can get away with nobody closely reading his charts, with the “(???, nothing)” business. Maybe I should throw in that box in my publications and see how that goes.
I feel like his arguments rely on the Barnum effect. He makes statements like “humans and LLMs predict the next word” and “evolution optimizes for reproduction” that are so vague that they can be assigned whatever meaning he wants. Because of this, you can’t easily dispel them (he just comes up with some different interpretation), and he can use them as carte blanche to justify whatever he wants.
He isn’t even trying with the yellow and orange boxes. What the fuck do “high-D toroidal attractor manifolds” and “6D helical manifolds” have to do with anything? Why are they there? And he really thinks he can get away with nobody closely reading his charts, with the “(???, nothing)” business. Maybe I should throw in that box in my publications and see how that goes.
It’s from another horseshit analogy that roughly boils down to both neural net inference (specifically when generating end-of-line tokens) and aspects of the biological components of human perception being somewhat geometrically modellable. I didn’t include the entire context or a link to the substack because I didn’t care to, but here is the analogy in full:
spoiler> The answer was: the AI represents various features of the line breaking process as one-dimensional helical manifolds in a six-dimensional space, then rotates the manifolds in some way that corresponds to multiplying or comparing the numbers that they’re representing. You don’t need to understand what this means, so I’ve relegated my half-hearted attempt to explain it to a footnote1. From our point of view, what’s important is that this doesn’t look like “LOL, it just sees that the last token was ree and there’s a 12.27% of a line break token following ree.” Next-token prediction created this system, but the system itself can involve arbitrary choices about how to represent and manipulate data. > > Human neuron interpretability is even harder than AI neuron interpretability, but probably your thoughts involve something at least as weird as helical manifolds in 6D spaces.I searched the literature for the closest human equivalent to Claude’s weird helical manifolds, and was able to find one team talking about how the entorhinal cells in the hippocampus, which help you track locations in 2D space, use “high-dimensional toroidal attractor manifolds”. You never think about these, and if Claude is conscious, it doesn’t think about its helices either2. These are just the sorts of strange hacks that next-token/next-sense-datum prediction algorithms discover to encode complicated concepts onto physical computational substrate.
re: the bolded part, I like how explicitly cherry-picking neuroscience passes for peak rationalism.
This somehow makes things even funnier. If he had any understanding of modern math, he would know that representing a set of things as points in some geometric space is one of the most common techniques in math. (A basic example: a pair of numbers can be represented by a point in 2D space.) Also, a manifold is an extremely broad mathematical concept: knowing that two things are manifolds does not meant that they are the same or remotely similar, without checking the details. There are tons of things you can model as a manifold if you try hard enough.
From what I see, Scoot read a paper modeling LLM inference with manifolds and thought “wow, cool!” Then he fished for neuroscience papers until he found one that modeled neurons using a fancy mathematical framework. Both of the papers have blah blah blah something something manifolds so there must be a deep connection!
It’s entirely possible he does get that it’s a nothing burger but is just being his usual disingenuous self to pull people in.
Amoeba_GirlJesus fucking christ I will don’t think I will ever get over how fucking dogshit the fucking rationalists are at epistemology
IT’S CALLED A FUCKING MAPPING. “MAP”. AS IN NOT THE TERRITORY. IT’S IN THE NAME.
I mean the whole entire premise (not unique to this post, scoot’s gotten a lot of mileage out of this) is shoehorning LLMs into the predictive coding framework mostly on the grounds that they both use prediction terminology and deal with work units that they call neurons, with the added bonus that PC posits Bayesian inference is involved so it’s obviously extra valid.
Queue a few thousand words of scoot wearing his science popularizer hat and just declaring the most vacuous shit imaginable with a straight face and a friendly teacher’s casual authority.
bad at epistemology
Gwern once denied chaos theory in a way that Freeman Dyson called out in 1985, and as LessWrongers go he is a pretty clear thinker!
what the hecky
he’s so offended he’s been told he’s not god!
geriksonThat’s such a weird comment… like “worried about hurricanes” - the first idea is to pour literal oil on the water??? in what world does that scale??? then it concludes with “maybe don’t build fragile buildings in hurricane areas” - lead with that you pillock
I feel I’m stepping into some long-forgotten debate on LW on alignment or something because there’s so much that doesn’t make sense in context